
CHAPTER 2. LITERATURE REVIEW
TensorFlow [26] is an open-source machine learning framework that has gained
widespread popularity for its flexibility and robustness. Developed by Google Brain,
TensorFlow provides a comprehensive ecosystem of tools, libraries, and commu-
nity resources that facilitate the development of machine learning and deep learn-
ing models. It allows researchers and developers to build, train, and deploy machine
learning models for a wide range of applications, from image and speech recogni-
tion to natural language processing and reinforcement learning. One of Tensor-
Flow’s key strengths is its support for both high-level APIs, like Keras for quick
model prototyping, and low-level APIs, offering fine-grained control over model
architecture and training. TensorFlow’s versatility makes it suitable for both begin-
ners and experts in the field of machine learning.
d. Keras
Keras [27] is an open-source high-level neural networks API written in Python.
It is designed to be user-friendly, modular, and easy to use, making it a popular
choice for both beginners and experts in the field of deep learning and artificial
intelligence. Keras acts as an interface for the TensorFlow library, as well as other
popular deep learning frameworks, allowing users to quickly and efficiently build
and train neural networks for various tasks, such as image classification, natural
language processing, and more. One of the key advantages of Keras is its simplic-
ity and abstraction. It provides a straightforward and intuitive way to define and
configure neural network models, with a focus on enabling rapid experimentation
and prototyping. With Keras, users can construct complex network architectures
by simply stacking together layers and specifying their configurations. Keras sup-
ports both CPU and GPU acceleration, making it suitable for various computing
environments. Additionally, it offers a wide range of pre-trained models and tools
for transfer learning, allowing users to leverage the knowledge gained from exist-
ing models to solve new and specific problems. Overall, Keras is a versatile and
accessible library for building and training deep learning models.
e. Sklearn
Scikit-Learn [28], commonly referred to as sklearn, is a renowned open-source
machine learning library for Python. It provides a wide array of efficient and user-
fr iendly tools for various aspects of machine learning, including classification, re-
gression, clustering, dimensionality reduction, and model selection and evaluation.
Scikit-Learn is characterized by its clean, consistent API, which makes it easy to
use, especially for those new to machine learning. It offers an extensive selection of
algor ithms for different tasks and supports the entire machine learning workflow,
from data preprocessing to model training and evaluation. With its comprehensive
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